A Multi-Graph Diffusion Mamba Framework for Ultra-Short-Term Wind Power Forecasting

Accurate ultra-short-term wind power forecasting is critical to grid security and economic dispatch. However, existing methodologies are constrained by two fundamental gaps: rigid topologies that fail to balance physical and data-driven information, and decoupled architectures that process spatial and temporal features separately. To address these gaps, this paper proposes a novel Multi-Graph Diffusion Mamba (MGDM) framework. A multi-graph architecture is introduced to decouple the dependencies among wind turbines into a physics-informed Meteorological Causal Graph (MCG) that captures directional wind propagation, and a learnable Systemic Adaptive Graph (SAG) that encodes unobserved operational constraints. A dynamic gated fusion mechanism adaptively balances the two graphs through a real-time confidence factor, and the fused representation is finally fed into a topology-aware Mamba module, where graph diffusion convolutions are embedded into the selective scan process, achieving joint spatio-temporal modeling. The results demonstrate that the MGDM outperforms all baseline models for 15 min, 1 h, 2 h, 3 h and 4 h ahead forecasts, and achieves improvements in accuracy of 3.59–4.81% across the forecasting horizons over the best benchmark model. Moreover, ablation studies demonstrate that the multi-graph topology and the selective scan mechanism are indispensable for robust forecasting. The gating factor is further validated to serve as an indicator of regime switching, reflecting the model’s dynamic balance between physical and data-driven graphs under varying scenarios. These results confirm that the MGDM delivers consistent improvements in both forecasting accuracy and model interpretability under diverse operating conditions in the evaluated wind farm.